

import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
	import torch

	__snake_case				:      Tuple   		=			True
except ImportError:
	__snake_case				:      Union[str, Any]   		=			False


try:
	from torch.hub import _get_torch_home

	__snake_case				:      Optional[Any]   		=			_get_torch_home()
except ImportError:
	__snake_case				:      Dict   		=			os.path.expanduser(
	    os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
	)

__snake_case				:      Any   		=			os.path.join(torch_cache_home, """transformers""")

__snake_case				:      Optional[int]   		=			"""https://cdn.huggingface.co"""
__snake_case				:      List[str]   		=			"""https://s3.amazonaws.com/models.huggingface.co/bert"""
__snake_case				:      Any   		=			"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
__snake_case				:      List[Any]   		=			os.path.join(PATH, """config.yaml""")
__snake_case				:      Tuple   		=			os.path.join(PATH, """attributes.txt""")
__snake_case				:      Any   		=			os.path.join(PATH, """objects.txt""")
__snake_case				:      str   		=			os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
__snake_case				:      str   		=			os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
__snake_case				:      int   		=			os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
__snake_case				:      Optional[Any]   		=			"""pytorch_model.bin"""
__snake_case				:      Optional[int]   		=			"""config.yaml"""


def 					_UpperCAmelCase				(						a__=OBJECTS  ,	a__=ATTRIBUTES):

			'''simple docstring'''



			a_						:						Any 							= []
			with open(a__) as f:
						for object in f.readlines():
									vg_classes.append(object.split(""",""")[0].lower().strip())

			a_						:						List[Any] 							= []
			with open(a__) as f:
						for object in f.readlines():
									vg_attrs.append(object.split(""",""")[0].lower().strip())
			return vg_classes, vg_attrs


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Dict 							= OrderedDict()
			with open(a__  ,	"""rb""") as f:
						a_						:						Tuple 							= pkl.load(a__)["""model"""]
			for k in copy.deepcopy(list(ckp.keys())):
						a_						:						int 							= ckp.pop(a__)
						if isinstance(a__  ,	np.ndarray):
									a_						:						Any 							= torch.tensor(a__)
						else:
									assert isinstance(a__  ,	torch.tensor), type(a__)
						a_						:						Union[str, Any] 							= v
			return r



class        A__:
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			{}





						def __init__(    self  ,      _lowercase  ,      _lowercase = "root"  ,      _lowercase=0    )     -> List[str]:
									a_						:						Tuple 							= name
									a_						:						str 							= level
									a_						:						Tuple 							= {}
									for k, v in dictionary.items():
												if v is None:
															raise ValueError()
												a_						:						Optional[int] 							= copy.deepcopy(_lowercase    )
												a_						:						str 							= copy.deepcopy(_lowercase    )
												if isinstance(_lowercase  ,      _lowercase    ):
															a_						:						Tuple 							= Config(_lowercase  ,      name=_lowercase  ,      level=level + 1    )
												a_						:						Optional[Any] 							= v
												setattr(self  ,      _lowercase  ,      _lowercase    )

									a_						:						int 							= d





						def __repr__(    self    )     -> str:
									return str(list((self._pointer.keys())    )    )





						def __setattr__(    self  ,      _lowercase  ,      _lowercase    )     -> Optional[int]:
									a_						:						Union[str, Any] 							= val
									a_						:						List[str] 							= val
									a_						:						List[str] 							= key.split("""."""    )
									a_						:						Union[str, Any] 							= len(_lowercase    ) - 1
									a_						:						int 							= self._pointer
									if len(_lowercase    ) > 1:
												for i, l in enumerate(_lowercase    ):
															if hasattr(self  ,      _lowercase    ) and isinstance(getattr(self  ,      _lowercase    )  ,      _lowercase    ):
																		setattr(getattr(self  ,      _lowercase    )  ,      """.""".join(levels[i:]    )  ,      _lowercase    )
															if l == last_level:
																		a_						:						Union[str, Any] 							= val
															else:
																		a_						:						Any 							= pointer[l]





						def 			UpperCamelCase__					(    self    )     -> Dict:
									return self._pointer





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> List[str]:
									with open(F'''{file_name}'''  ,      """w"""    ) as stream:
												dump(_lowercase  ,      _lowercase    )





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> Optional[Any]:
									with open(F'''{file_name}'''  ,      """w"""    ) as stream:
												json.dump(_lowercase  ,      _lowercase    )





						@staticmethod
						def 			UpperCamelCase__					(    _lowercase    )     -> Optional[int]:
									with open(_lowercase    ) as stream:
												a_						:						str 							= load(_lowercase  ,      Loader=_lowercase    )
									return data





						def __str__(    self    )     -> int:
									a_						:						List[str] 							= """    """
									if self._name != "root":
												a_						:						Dict 							= F'''{t * (self._level-1)}{self._name}:\n'''
									else:
												a_						:						Dict 							= """"""
									a_						:						str 							= self._level
									for i, (k, v) in enumerate(self._pointer.items()    ):
												if isinstance(_lowercase  ,      _lowercase    ):
															r += F'''{t * (self._level)}{v}\n'''
															self._level += 1
												else:
															r += F'''{t * (self._level)}{k}: {v} ({type(_lowercase    ).__name__})\n'''
												a_						:						Optional[Any] 							= level
									return r[:-1]





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      **_lowercase    )     -> List[Any]:
									a_							,					a_						:						Dict 							= cls.get_config_dict(_lowercase  ,      **_lowercase    )
									return cls(_lowercase    )





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      **_lowercase    )     -> str:
									a_						:						Tuple 							= kwargs.pop("""cache_dir"""  ,      _lowercase    )
									a_						:						Optional[Any] 							= kwargs.pop("""force_download"""  ,      _lowercase    )
									a_						:						int 							= kwargs.pop("""resume_download"""  ,      _lowercase    )
									a_						:						Optional[Any] 							= kwargs.pop("""proxies"""  ,      _lowercase    )
									a_						:						int 							= kwargs.pop("""local_files_only"""  ,      _lowercase    )

									if os.path.isdir(_lowercase    ):
												a_						:						Dict 							= os.path.join(_lowercase  ,      _lowercase    )
									elif os.path.isfile(_lowercase    ) or is_remote_url(_lowercase    ):
												a_						:						List[Any] 							= pretrained_model_name_or_path
									else:
												a_						:						Any 							= hf_bucket_url(_lowercase  ,      filename=_lowercase  ,      use_cdn=_lowercase    )

									try:
												# Load from URL or cache if already cached
												a_						:						Union[str, Any] 							= cached_path(
												    _lowercase  ,      cache_dir=_lowercase  ,      force_download=_lowercase  ,      proxies=_lowercase  ,      resume_download=_lowercase  ,      local_files_only=_lowercase  ,      )
												# Load config dict
												if resolved_config_file is None:
															raise EnvironmentError

												a_						:						int 							= Config.load_yaml(_lowercase    )

									except EnvironmentError:
												a_						:						List[str] 							= """Can't load config for"""
												raise EnvironmentError(_lowercase    )

									if resolved_config_file == config_file:
												print("""loading configuration file from path"""    )
									else:
												print("""loading configuration file cache"""    )

									return Config.load_yaml(_lowercase    ), kwargs







def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Tuple 							= torch.load("""dump.pt"""  ,	map_location=in_tensor.device)
			a_						:						Dict 							= in_tensor.numpy()
			a_						:						Optional[Any] 							= out_tensor.numpy()[0]
			print(na.shape  ,	na[0, 0, :5])
			print(na.shape  ,	na[0, 0, :5])
			assert np.allclose(a__  ,	a__  ,	rtol=0.01  ,	atol=0.1), (
			    f'''{sum([1 for x in np.isclose(a__  ,	a__  ,	rtol=0.01  ,	atol=0.1).flatten() if x is False])/len(na.flatten())*1_0_0:.4f} %'''
			    " element-wise mismatch"
			)
			raise Exception("""tensors are all good""")

			# Hugging face functions below


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						int 							= urlparse(a__)
			return parsed.scheme in ("http", "https")


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=True):

			'''simple docstring'''



			a_						:						Tuple 							= CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
			a_						:						Optional[int] 							= """/""" not in model_id
			if legacy_format:
						return f'''{endpoint}/{model_id}-{filename}'''
			else:
						return f'''{endpoint}/{model_id}/{filename}'''


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=None  ,	a__=0  ,	a__=None  ,	):

			'''simple docstring'''



			a_						:						Optional[int] 							= """python/{}""".format(sys.version.split()[0])
			if _torch_available:
						ua += "; torch/{}".format(torch.__version__)
			if isinstance(a__  ,	a__):
						ua += "; " + "; ".join("""{}/{}""".format(a__  ,	a__) for k, v in user_agent.items())
			elif isinstance(a__  ,	a__):
						ua += "; " + user_agent
			a_						:						str 							= {"""user-agent""": ua}
			if resume_size > 0:
						a_						:						List[str] 							= """bytes=%d-""" % (resume_size,)
			a_						:						str 							= requests.get(a__  ,	stream=a__  ,	proxies=a__  ,	headers=a__)
			if response.status_code == 4_1_6:  # Range not satisfiable
						return
			a_						:						Dict 							= response.headers.get("""Content-Length""")
			a_						:						Tuple 							= resume_size + int(a__) if content_length is not None else None
			a_						:						Tuple 							= tqdm(
			    unit="""B"""  ,	unit_scale=a__  ,	total=a__  ,	initial=a__  ,	desc="""Downloading"""  ,	)
			for chunk in response.iter_content(chunk_size=1_0_2_4):
						if chunk:  # filter out keep-alive new chunks
									progress.update(len(a__))
									temp_file.write(a__)
			progress.close()


def 					_UpperCAmelCase				(						a__  ,	a__=None  ,	a__=False  ,	a__=None  ,	a__=1_0  ,	a__=False  ,	a__=None  ,	a__=False  ,	):

			'''simple docstring'''



			if cache_dir is None:
						a_						:						Dict 							= TRANSFORMERS_CACHE
			if isinstance(a__  ,	a__):
						a_						:						int 							= str(a__)

			os.makedirs(a__  ,	exist_ok=a__)

			a_						:						Dict 							= None
			if not local_files_only:
						try:
									a_						:						Tuple 							= requests.head(a__  ,	allow_redirects=a__  ,	proxies=a__  ,	timeout=a__)
									if response.status_code == 2_0_0:
												a_						:						Any 							= response.headers.get("""ETag""")
						except (EnvironmentError, requests.exceptions.Timeout):
									# etag is already None
									pass

			a_						:						List[Any] 							= url_to_filename(a__  ,	a__)

			# get cache path to put the file
			a_						:						Union[str, Any] 							= os.path.join(a__  ,	a__)

			# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
			# try to get the last downloaded one
			if etag is None:
						if os.path.exists(a__):
									return cache_path
						else:
									a_						:						Any 							= [
									    file
									    for file in fnmatch.filter(os.listdir(a__)  ,	filename + """.*""")
									    if not file.endswith(""".json""") and not file.endswith(""".lock""")
									]
									if len(a__) > 0:
												return os.path.join(a__  ,	matching_files[-1])
									else:
												# If files cannot be found and local_files_only=True,
												# the models might've been found if local_files_only=False
												# Notify the user about that
												if local_files_only:
															raise ValueError(
															    """Cannot find the requested files in the cached path and outgoing traffic has been"""
															    """ disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
															    """ to False.""")
												return None

    # From now on, etag is not None.
			if os.path.exists(a__) and not force_download:
						return cache_path

			# Prevent parallel downloads of the same file with a lock.
			a_						:						Optional[int] 							= cache_path + """.lock"""
			with FileLock(a__):
						# If the download just completed while the lock was activated.
						if os.path.exists(a__) and not force_download:
									# Even if returning early like here, the lock will be released.
									return cache_path

						if resume_download:
									a_						:						List[Any] 							= cache_path + """.incomplete"""

									@contextmanager
									def _resumable_file_manager():
												with open(a__  ,	"""a+b""") as f:
															yield f

									a_						:						List[Any] 							= _resumable_file_manager
									if os.path.exists(a__):
												a_						:						Optional[int] 							= os.stat(a__).st_size
									else:
												a_						:						Union[str, Any] 							= 0
						else:
									a_						:						Optional[Any] 							= partial(tempfile.NamedTemporaryFile  ,	dir=a__  ,	delete=a__)
									a_						:						List[str] 							= 0

						# Download to temporary file, then copy to cache dir once finished.
						# Otherwise you get corrupt cache entries if the download gets interrupted.
						with temp_file_manager() as temp_file:
									print(
									    """%s not found in cache or force_download set to True, downloading to %s"""  ,	a__  ,	temp_file.name  ,	)

									http_get(
									    a__  ,	a__  ,	proxies=a__  ,	resume_size=a__  ,	user_agent=a__  ,	)

						os.replace(temp_file.name  ,	a__)

						a_						:						Tuple 							= {"""url""": url, """etag""": etag}
						a_						:						Tuple 							= cache_path + """.json"""
						with open(a__  ,	"""w""") as meta_file:
									json.dump(a__  ,	a__)

			return cache_path


def 					_UpperCAmelCase				(						a__  ,	a__=None):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= url.encode("""utf-8""")
			a_						:						str 							= shaaaa(a__)
			a_						:						str 							= url_hash.hexdigest()

			if etag:
						a_						:						Union[str, Any] 							= etag.encode("""utf-8""")
						a_						:						str 							= shaaaa(a__)
						filename += "." + etag_hash.hexdigest()

			if url.endswith(""".h5"""):
						filename += ".h5"

			return filename


def 					_UpperCAmelCase				(						a__  ,	a__=None  ,	a__=False  ,	a__=None  ,	a__=False  ,	a__=None  ,	a__=False  ,	a__=False  ,	a__=False  ,	):

			'''simple docstring'''



			if cache_dir is None:
						a_						:						Optional[int] 							= TRANSFORMERS_CACHE
			if isinstance(a__  ,	a__):
						a_						:						List[Any] 							= str(a__)
			if isinstance(a__  ,	a__):
						a_						:						Optional[int] 							= str(a__)

			if is_remote_url(a__):
						# URL, so get it from the cache (downloading if necessary)
						a_						:						Optional[int] 							= get_from_cache(
						    a__  ,	cache_dir=a__  ,	force_download=a__  ,	proxies=a__  ,	resume_download=a__  ,	user_agent=a__  ,	local_files_only=a__  ,	)
			elif os.path.exists(a__):
						# File, and it exists.
						a_						:						int 							= url_or_filename
			elif urlparse(a__).scheme == "":
						# File, but it doesn't exist.
						raise EnvironmentError("""file {} not found""".format(a__))
			else:
						# Something unknown
						raise ValueError("""unable to parse {} as a URL or as a local path""".format(a__))

			if extract_compressed_file:
						if not is_zipfile(a__) and not tarfile.is_tarfile(a__):
									return output_path

						# Path where we extract compressed archives
						# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
						a_							,					a_						:						str 							= os.path.split(a__)
						a_						:						List[str] 							= output_file.replace("""."""  ,	"""-""") + """-extracted"""
						a_						:						List[str] 							= os.path.join(a__  ,	a__)

						if os.path.isdir(a__) and os.listdir(a__) and not force_extract:
									return output_path_extracted

						# Prevent parallel extractions
						a_						:						str 							= output_path + """.lock"""
						with FileLock(a__):
									shutil.rmtree(a__  ,	ignore_errors=a__)
									os.makedirs(a__)
									if is_zipfile(a__):
												with ZipFile(a__  ,	"""r""") as zip_file:
															zip_file.extractall(a__)
															zip_file.close()
									elif tarfile.is_tarfile(a__):
												a_						:						int 							= tarfile.open(a__)
												tar_file.extractall(a__)
												tar_file.close()
									else:
												raise EnvironmentError("""Archive format of {} could not be identified""".format(a__))

						return output_path_extracted

			return output_path


def 					_UpperCAmelCase				(						a__  ,	a__=","):

			'''simple docstring'''



			assert isinstance(a__  ,	a__)
			if os.path.isfile(a__):
						with open(a__) as f:
									a_						:						List[Any] 							= eval(f.read())
			else:
						a_						:						List[Any] 							= requests.get(a__)
						try:
									a_						:						Tuple 							= requests.json()
						except Exception:
									a_						:						Union[str, Any] 							= req.content.decode()
									assert data is not None, "could not connect"
									try:
												a_						:						Union[str, Any] 							= eval(a__)
									except Exception:
												a_						:						Union[str, Any] 							= data.split("""\n""")
						req.close()
			return data


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= requests.get(a__)
			a_						:						Union[str, Any] 							= np.array(Image.open(BytesIO(response.content)))
			return img


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Dict 							= url.split("""/""")[-1]
			if fn not in os.listdir(os.getcwd()):
						wget.download(a__)
			with open(a__  ,	"""rb""") as stream:
						a_						:						Optional[int] 							= pkl.load(a__)
			a_						:						Optional[Any] 							= weights.pop("""model""")
			a_						:						Union[str, Any] 							= {}
			for k, v in model.items():
						a_						:						str 							= torch.from_numpy(a__)
						if "running_var" in k:
									a_						:						Union[str, Any] 							= torch.tensor([0])
									a_						:						Union[str, Any] 							= k.replace("""running_var"""  ,	"""num_batches_tracked""")
									a_						:						int 							= zero
			return new


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			print(f'''{os.path.abspath(os.path.join(a__  ,	os.pardir))}/demo.ipynb''')


def 					_UpperCAmelCase				(						a__  ,	a__="RGB"):

			'''simple docstring'''



			assert isinstance(a__  ,	a__)
			if os.path.isfile(a__):
						a_						:						Union[str, Any] 							= cva.imread(a__)
			else:
						a_						:						List[str] 							= get_image_from_url(a__)
						assert img is not None, f'''could not connect to: {im}'''
			a_						:						str 							= cva.cvtColor(a__  ,	cva.COLOR_BGR2RGB)
			if input_format == "RGB":
						a_						:						Dict 							= img[:, :, ::-1]
			return img



def 					_UpperCAmelCase				(						a__  ,	a__=1):

			'''simple docstring'''



			return (images[i : i + batch] for i in range(0  ,	len(a__)  ,	a__))





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:      Any   		=			logging.get_logger(__name__)

__snake_case				:      Union[str, Any]   		=			{
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class        A__(a_							):
						"""simple docstring"""






						_A    :				Union[str, Any]       					=			'''cvt'''





						def __init__(    self  ,      _lowercase=3  ,      _lowercase=[7, 3, 3]  ,      _lowercase=[4, 2, 2]  ,      _lowercase=[2, 1, 1]  ,      _lowercase=[64, 192, 384]  ,      _lowercase=[1, 3, 6]  ,      _lowercase=[1, 2, 10]  ,      _lowercase=[4.0, 4.0, 4.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.0]  ,      _lowercase=[0.0, 0.0, 0.1]  ,      _lowercase=[True, True, True]  ,      _lowercase=[False, False, True]  ,      _lowercase=["dw_bn", "dw_bn", "dw_bn"]  ,      _lowercase=[3, 3, 3]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[2, 2, 2]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=[1, 1, 1]  ,      _lowercase=0.0_2  ,      _lowercase=1e-12  ,      **_lowercase  ,      )     -> Tuple:
									super().__init__(**_lowercase    )
									a_						:						str 							= num_channels
									a_						:						Optional[int] 							= patch_sizes
									a_						:						Dict 							= patch_stride
									a_						:						int 							= patch_padding
									a_						:						int 							= embed_dim
									a_						:						Tuple 							= num_heads
									a_						:						Optional[Any] 							= depth
									a_						:						str 							= mlp_ratio
									a_						:						Optional[int] 							= attention_drop_rate
									a_						:						int 							= drop_rate
									a_						:						List[Any] 							= drop_path_rate
									a_						:						List[Any] 							= qkv_bias
									a_						:						Dict 							= cls_token
									a_						:						Union[str, Any] 							= qkv_projection_method
									a_						:						List[Any] 							= kernel_qkv
									a_						:						Optional[Any] 							= padding_kv
									a_						:						Any 							= stride_kv
									a_						:						Dict 							= padding_q
									a_						:						Optional[int] 							= stride_q
									a_						:						Tuple 							= initializer_range
									a_						:						int 							= layer_norm_eps


